Getting Serious about the Development of Computational Humor
Oliviero Stock and Carlo Strapparava
ITC-irst, 1-38050 Povo Trento Italy
{stock, strappa}@itc.it
Abstract
Society needs humor, not just for entertainment. In
the Web age, presentations become more and more
flexible and personalized and they will require hu­
mor contributions for electronic commerce devel­
opments (e.g. product promotion, getting selective
attention, help in memorizing names, etc...) more
or less as it happened in the world of broadcasted
advertisement. Even if deep modeling of humor in
all of its facets is not something for the near future,
there is something concrete that has been achieved
and that can help in providing attention to the field.
The paper refers to the results of HAHACRONYM,
a project devoted to humorous acronym production,
a circumscribed task that nonetheless requires vari­
ous generic components. The project opens the way
to developments for creative language, with appli­
cations in the world of advertisement.
1 Introduction
Future human-machine interaction, according to a
widespread view, will emphasize naturalness and effec­
tiveness and hence the incorporation of models of possibly
all human cognitive capabilities, including the handling of
humor. There are many practical settings where computa­
tional humor will add value. Among them: business world
applications (e-commerce to name one), general computermediated communication and human-computer interaction
[Morkes et al, 1999], educational and edutainment systems.
There are important prospects for humor also in automatic
information presentation. In the Web age presentations
become more and more flexible and personalized and they
will require humor contributions for electronic commerce
developments (e.g. product promotion, getting selective
attention, help in memorizing names, etc...) more or less as
it happened in the world of broadcasted advertisement.
Yet deep modeling of humor in all of its facets is not some­
thing for the near future; the phenomena are too complex,
humor is one of the most sophisticated forms of human in­
telligence. It is Al-complete: the problem of modeling it is
as difficult to solve as the most difficult Artificial Intelligence
problems. But some steps can be followed to achieve results.
In the general case, in order to be successfully humorous,
ART AND CREATIVITY
a computational system should be able to: recognize situa­
tions appropriate for humor; choose a suitable kind of humor
for the situation; generate an appropriately humorous output;
and, if there is some form of interaction or control, evaluate
the feedback.
We are concerned with systems that automatically produce
humorous output (rather than systems that appreciate humor).
Some of the fundamental competencies are within the range
of the state of the art of natural language processing. In one
form or in another humor is most often based on some form
of incongruity. For verbal humor this means that different in­
terpretations of utterances must be possible (and must not be
detected before the culmination of the humorous process) or
must cause perception of specific forms of opposition. Nat­
ural language processing research has often dealt with ambi­
guity in language. A common view is that ambiguity is an
obstacle for deep comprehension. Most current text process­
ing systems attempt to reduce the number of possible inter­
pretations of the sentences, and a failure to do so is seen as
a weakness of the system. From a different point of view,
the potential for ambiguity can be seen as a positive feature
of natural language. Metaphors, idioms, poetic language and
humor use all the multiple senses of texts to suggest connec­
tions between concepts that cannot, or should not, be stated
explicitly.
The work presented here is based on various resources for
natural language processing, adapted for humor. It is a small
step, but aiming at an appreciable concrete result. It has
been developed within HAHACRONYM1, a project devoted
to computational humor. A visible and evaluable result was
at the basis of the deal. We proposed a situation that is of
practical interest, where there is no domain restriction and
many components are present, but simpler than in more ex­
tended scenarios. The goal is a system that makes fun of ex­
isting acronyms, or, starting from concepts provided by the
user, produces a new acronym, constrained to be a word of
the given language. And, of course, it has to be funny.
The project was meant to convince about the potential of
computational humor, through the demonstration of a work­
ing prototype and an assessment of the state of the art and
1
European Project IST-2000-30039. HAHACRONYM has been
the first EU project devoted to computational humor. The consor­
tium included ITC-irst, as coordinator, and the University of Twente.
See h t t p : / / h a h a . i t c . i t .
59
of scenarios where humor can add something to existing in­
formation technologies. The results of the project put us in
a better position to move forwards in introducing computa­
tional humor in more complex scenarios.
2 AI and Computational Humor
So far only very limited effort has been put on building com­
putational humor prototypes. Indeed very few working pro­
totypes that process humorous text and/or simulate humor
mechanisms exist, and mostly they worked in very restricted
domains.
There has been a considerable amount of research on lin­
guistics of humor and on theories of semantics and prag­
matics of humor [Attardo, 1994; Attardo and Raskin, 1991;
Giora and Fein, 1999; Attardo, 2002]; however, most of the
work has not been formal enough to be used directly for com­
putational humor modeling. An effort toward formalization
of forced reinterpretation jokes has been presented by Ritchie
[2002].
Within the artificial intelligence community, most writ­
ing on humor has been speculative [Minsky, 1980; Hofstadter el al., 1989]. Minsky made some preliminary remarks
about how humor could be viewed from the artificial intelli­
gence/cognitive science perspective, refining Freud's notion
that humor is a way of bypassing our mental "censors" which
control inappropriate thoughts and feelings. Utsumi [1996]
outlines a logical analysis of irony, but this work has not been
implemented. Among other works: De Palma and Weiner
[ 1992] have worked on knowledge representation of riddles,
Ephratt [1990] has constructed a program that parses a lim­
ited range of ambiguous sentences and detects alternative hu­
morous readings. A formalization, based on a cognitive ap­
proach (the belief-desire-intention model), distinguishing be­
tween real and fictional humor has been provided by Mele
[2002].
Probably the most important attempt to create a computa­
tional humor prototype is the work of Binsted and Ritchie
[1997]. They have devised a formal model of the seman­
tic and syntactic regularities underlying some of the simplest
types of punning riddles. A punning riddle is a questionanswer riddle that uses phonological ambiguity. The three
main strategies used to create phonological ambiguity are syl­
lable substitution, word substitution and metathesis.
Almost all the computational approaches try to deal with
the incongruity theory at various level of refinement [Raskin,
1985; Attardo, 1994]. The incongruity theory focuses on the
element of surprise. It states that humor is created out of a
conflict between what is expected and what actually occurs
in the joke. This accounts for the most obvious features of a
large part of humor phenomena: ambiguity or double mean­
ing.
Specific workshops concerned with Computational Humor
have taken place in recent years and have drawn together most
of the community active in the field. The proceedings of the
most comprehensive events are [Hulstijn and Nijholt, 1996]
and iStock et al, 2002]. Ritchie [2001] has published a sur­
vey of the state of the art in the field.
60
3
HAHACRONYM
3.1 Resources
In order to realize the HAHACRONYM prototype, we have re­
fined existing resources and we have developed general tools
useful for humorous systems. As we will see, a fundamental
tool is an incongruity detector/generator, that makes the sys­
tem able to detect semantic mismatches between word mean­
ing and sentence meaning (i.e. in our case the acronym and
its context). For all tools, particular attention was put on
reusability.
The starting point for us consisted in making use of some
"off-the-shelf resources, such as W O R D N E T DOMAINS
[Magnini et al., 2002] (an extension of the well-known En­
glish WORDNET) and standard parsing techniques. The tools
resulting from the adaptation will be reusable for other appli­
cations, and are portable straightforwardly to other languages
(e.g. WORDNET DOMAINS is multilingual).
Wordnet and Wordnet Domains
WORDNET is a thesaurus for the English language inspired
by psycholinguistics principles and developed at the Prince­
ton University by George Miller [Fellbaum, 1998]. It has
been conceived as a computational resource, therefore im­
proving some of the drawbacks of traditional dictionaries,
such as circularity of definitions and ambiguity of sense ref­
erences. Lemmata (about 130,000 for version 1.6) are orga­
nized in synonym classes (about 100,000 synsets). WORDN E T can be described as a "lexical matrix" with two di­
mensions: a dimension for lexical relations, that is rela­
tions holding among words, and therefore language spe­
cific, and a dimension for conceptual relations, which hold
among senses (the synsets) and that, at least in part, we con­
sider independent from a particular language. A synset con­
tains all the words by means of which it is possible to ex­
press the synset meaning: for example the synset {horse,
Equus-caballus} describes the sense of "horse" as an
animal, while the synset { k n i g h t , horse} describes
the sense of "horse" as a chessman. The main relations
present in WORDNET are synonymy, antonymy, hyperonymyhypony my, meronymy-holonymy, entailment, troponymy.
Augmenting WordNet with Domain information
Domains have been used both in linguistics (i.e. Semantic
Fields) and in lexicography (i.e. Subject Field Codes) to mark
technical usages of words. Although this is useful informa­
tion for sense discrimination, in dictionaries it is typically
used for a small portion of the lexicon. WORDNET D O ­
MAINS is an attempt to extend the coverage of domain labels
within an already existing lexical database, WORDNET (ver­
sion 1.6). The synsets have been annotated with at least one
domain label, selected from a set of about two hundred la­
bels hierarchically organized. (Figure 1 shows a portion of
the domain hierarchy.)
We have organized about 250 domain labels in a hierar­
chy (exploiting Dewey Decimal Classification), where each
level is made up of codes of the same degree of specificity:
for example, the second level includes domain labels such as
BOTANY, LINGUISTICS, HISTORY, SPORT and RELIGION,
while at the third level we can find specialization such as
ART AND CREATIVITY
Figure 2: An example of adjective clusters linked by
antonymy relation
Figure 1: A portion of the domain hierarchy
A M E R I C A N _ H I S T O R Y , G R A M M A R , P H O N E T I C S and T E N NIS.
Information brought by domains is complementary to what
is already present in W O R D N E T . First of all a domain
may include synsets of different syntactic categories: for
instance M E D I C I N E groups together senses from Nouns,
such as d o c t o r # l and h o s p i t a l # l , and from Verbs
such as o p e r a t e # 7 . Second, a domain may include
senses from different W O R D N E T sub-hierarchies For example, SPORT contains senses such as a t h l e t e # l , deriving from l i f e _ f o r m # l , game e q u i p m e n t # l, from
p h y s i c a l _ o b j e c t # l , s p o r t # l from a c t # 2 , and
p l a y i n g _ f i e l d # l , from l o c a t i o n # l .
Opposition of semantic fields
On the basis of well recognized properties of humor accounted for in many theories (e.g. incongruity, semantic field
opposition, apparent contradiction, absurdity) we have modelled an independent structure of domain opposition, such as
R E L I G I O N VS. T E C H N O L O G Y , S E X VS. R E L I G I O N , etc... We
exploit these kind of opposition as a basic resource for the
incongruity generator.
Adjectives and Antonymy Relations
Adjectives play an important role in modifying and generating funny acronyms. W O R D N E T divides adjectives
into two categories.
Descriptive adjectives (e.g. b i g ,
b e a u t i f u l , i n t e r e s t i n g , possible, married)
constitute by far the largest category. The second category
is called simply relational adjectives because they are related
by derivation to nouns (i.e. e l e c t r i c a l in e l e c t r i c a l
e n g i n e e r i n g is related to noun e l e c t r i c i t y ) . To relational adjectives, strictly dependent on noun meanings, it is
often possible to apply similar strategies as those exploited
for nouns. Their semantic organization, though, is entirely
different from the one of the other major categories. In fact
it is not clear what it would mean to say that one adjective
"is a kind of' (ISA) some other adjective. The basic semantic
relation among descriptive adjectives is antonymy. W O R D N E T proposes also that this kind of adjectives is organized
in clusters of synsets associated by semantic similarity to a
focal adjective. Figure 2 shows clusters of adjectives around
the direct antonyms fast I slow.
ART AND CREATIVITY
Exploiting the hierarchy
It is possible to exploit the network of lexical and semantic relations built in W O R D N E T to make simple ontological
reasoning. For example, if a noun or an adjective has a geographic location meaning, the pertaining country and continent can be inferred.
Rhymes
The HAHACRONYM prototype takes into account
word rhymes and the rhythm of the acronym expansion.
To cope with this aspect we got and
reorganized
the
CMU
pronouncing
dictionary
( h t t p : //www. s p e e c h . e s . c m u . e d u / c g i - b i n / c m u d i c t )
with a suitable indexing. The CMU Pronouncing Dictionary
is a machine-readable pronunciation dictionary for North
American English that contains over 125,000 words and their
transcriptions.
Its format is particularly useful for speech recognition and
synthesis, as it has mappings from words to their pronunciations in the given phoneme set. The current phoneme set
contains 39 phonemes; vowels may carry lexical stress.
Parser, grammar and morphological analyzer
Word sequences that are at the basis of acronyms are subject to a well-defined grammar, simpler than a complete noun
phrase grammar, but complex enough to require a nontrivial
analyzer. We have decided to use a well established nondeterministic parsing technique. As far as the dictionary is concerned, we use the full W O R D N E T lexicon, integrated with
an ad-hoc morphological analyzer.
Also for the generation part we exploit the grammar as the
source for syntactic constraints.
All the components are implemented in Common Lisp augmented with nondeterministic constructs.
Other resources
An "a-semantic" dictionary is a collection of hyperbolic/epistemic/deontic adjective/adverbs. This is a last resource, that some time can be useful in the generation
of new acronyms. Some examples are: abnormally, abstrusely, adorably, exceptionally, exorbitantly, exponentially,
extraordinarily, voraciously, weirdly, wonderfully. This resource is hand-made, using various dictionaries as information sources.
Other lexical resources are: a euphemism dictionary, a
proper noun dictionary, lists of typical foreign words commonly used in the language with some strong connotation.
61
3.2
Architecture and Implementation
To get an ironic or "profaning" re-analysis of a given
acronym, the system follows various steps and strategies. The
main elements of the algorithm can be schematized as fol­
lows:
• acronym parsing and construction of a logical form
• choice of what to keep unchanged (typically the head
of the highest ranking NP) and what to modify (e.g. the
adjectives)
• look up for possible substitutions
• exploitation of semantic field oppositions
• granting phonological analogy: while keeping the con­
straint on the initial letters of the words, the overall
rhyme and rhythm should be preserved (the modified
acronym should sound similar to the original as much
as possible)
we can use in the acronym expansion a synonym of "CPU".
The same happens for "fast"). Once we have an acronym
proposal, a syntactic skeleton has to be filled to get a correct
noun phrase. For example given in input "fast" and "CPU",
the system selects TORPID with the possible syntactic skele­
ton:
• exploitation of W O R D N E T antonymy clustering for ad­
jectives
• use of the a-semantic dictionary as a last resource
Figures 3 and 4 show a sketch of the system architecture.
where "rapid" and "processor" are synonyms re­
spectively of "fast" and "CPU" and the notation
< P a r t _ o f _Speech>Le^tr means a word of that par­
ticular part_of.speech with Letter as initial. Then the system
fills this syntactic skeleton with strategies similar to those
described for re-analysis.
The system is fully implemented in Common Lisp, exploit­
ing CLOS and the Meta-Object protocol to import W O R D N E T DOMAINS and to implement the navigation/reasoning
algorithms.
3.3
In our system, making fun of existing acronyms amounts to
an ironical rewriting, desecrating them with some unexpect­
edly contrasting, but otherwise consistently sounding expan­
sion.
As far as acronym generation is concerned, the problem is
more complex. To make the task more attractive - and dif­
ficult - we constrain resulting acronyms to be words of the
dictionary (APPLE would be good, IBM would not). The
system takes in input concepts (actually synsets, possibly the
result of some other process, for instance sentence interpre­
tation) and some minimal structural indication, such as the
semantic head. The primary strategy of the system is to con­
sider as potential acronyms words that are in ironic relation
with the input concepts. By definition acronyms have to sat­
isfy constraints - to include the initial letters of some lexical
realization of the inputs words synsets, granting that the se­
quence of initials satisfy the overall acronym syntax. In this
primary strategy, the ironic reasoning comes mainly at the
level of acronym choice in the lexicon and in the selection of
the fillers of the open slots in the acronym.
For example, giving as input "fast" and "CPU", we
get static, torpid, dormant.
(Note that the com­
plete synset for "CPU" is { p r o c e s s o r # 3 , CPU#1,
c e n t r a l . . p r o c e s s i n g . u n i t # l , m a i n f r a m e # 2 } . So
62
Examples
Here below some examples of acronym re-analysis are re­
ported. As far as semantic field opposition is concerned we
have slightly tuned the system towards the domains F O O D ,
R E L I G I O N and SEX. We report the original acronym, the reanalysis and some comments about the strategies followed by
the system.
ACM - Association for Computing Machinery
- - > A s s o c i a t i o n f o r Confusing Machinery
F B I - Federal Bureau of Investigation
—► F a n t a s t i c Bureau of I n t i m i d a t i o n
The system keeps all the main heads and works on the adjec­
tives and the PP head, preserving the rhyme and/or using the
a-semantic dictionary.
CRT - Cathodic Ray Tube
—► C a t h o l i c Ray Tube
ESA - European Space Agency
—> Epicurean Space Agency
PDA - Personal Digital Assistant
—►Penitential Demoniacal A s s i s t a n t
—> P r e n u p t i a l D e v o t i o n a l A s s i s t a n t
MIT - Massachusetts Institute of Technology
—► M y t h i c a l I n s t i t u t e of Theology
Some re-analyses are RELIGION oriented. Note the rhymes.
ART AND CREATIVITY
As far as generation from scratch is concerned, a main con­
cept and some attributes (in terms of synsets) are given as in­
put to the system. Here below we report some examples of
acronym generation.
Main concept: processor (in the sense of CPU);
Attribute: fast
OPEN - On-line Processor for Effervescent Net
PIQUE - Processor for Immobile Quick Uncertain Experi­
mentation
TORPID - Traitorously Outstandingly Rusty Processor for
Inadvertent Data_proeessing
UTMOST - Unsettled Transcendental Mainframe for Off-line
Secured Tcp/ip
We note that the system tries to keep all the expansions
of the acronym coherent in the same semantic field of the
main concept (COMPUTER _SCIENCH). At the same time,
whenever possible, it exploits some incongruity in the lexi­
cal choices.
3.4
Evaluation
Testing the humorous quality of texts is not an easy task.
There have been relevant studies though, such as (Ruch,
19961. For HAHAc RONYM, a simpler case, an evaluation
was conducted under the supervision of Salvatore Attardo at
Youngstown University, Ohio. Both reanalysis and genera­
tion have been tested according to criteria of success stated in
advance and in agreement with the European Commission, at
the beginning of the project.
The participants in the evaluation were forty students.
They were all native speakers of English. The students were
not told that the acronyms had been computer generated. No
record was kept of which student had given which set of
answers (the answers were strictly anonymous). No demo­
graphic data were collected. However, generally speaking,
the group was homogeneous for age (traditional students, be­
tween the ages of 19 and 24) and mixed for gender and race.
The students were divided in two groups. The first group of
twenty was presented the reanalysis and generation data. We
tested about 80 reanalyzed and 80 generated acronyms (over
twice as many as required by the agreement with the Euro­
pean Commission). Both the reanalysis module and the gen­
eration module were found to be successful according to the
criteria spelled out in the assessment protocol. The acronyms
reanalysis module showed roughly 70% of acronyms hav­
ing a score of 55 or higher (out of a possible 100 points),
while the acronym generation module showed roughly 53%
of acronyms having a score of 55 or higher. The thresholds
for success established in the protocol were 60% and 45%,
respectively.
A set of randomly generated acronyms were presented to a
different group of twenty students. A special run of the sys­
tem was performed in which the semantic filters and heuris­
tics had been disabled, while only the syntactical constraints
were operational. (If the syntactical rules had been disabled
as well the output would have been gibberish and it would be
difficult to compare with the regular system production). In
ART AND CREATIVITY
Table 1: Evaluation Results
A curiosity that may be worth mentioning: HA­
HACRONYM participated to a contest about (human) produc­
tion of best acronyms, organized in December 2002 by RAI,
the Italian National Broadcasting Service. The system won a
jury's special prize.
4 Prospects for advertisement
Humor is the healthy way of creating 'distance* between
one's self and the problem, a way of standing back and look­
ing at the problem with perspective. Humor reveals new as­
pects, disarms and relaxes. It is also infectious and it is an
important way to communicate ideas. On the cognitive side
humor has two very important properties:
- it helps getting and keeping people's attention. Type
and rhythm of humor may vary and the time involved
in building the effect may be different in different cases:
some times there is a context - like joke telling - that
from the beginning let you expect for the humorous cli­
max, which may occur after a long while: other times the
effect is obtained in almost no time, with one perceptive
act - for instance in static visual humor, funny posters
or in cases when some well established convention is re­
versed with an utterance;
- it helps remembering. For instance it is a common ex­
perience to connect in our memory some knowledge we
have acquired to a humorous remark or event. In a for­
eign language acquisition it may happen that an involun­
tary funny situation is created because of so called "false
friends" - words that sound similar in two languages and
may have the same origin but have a very different mean­
ing. The humorous experience is conducive to remem­
bering the correct use of the word.
No wonder that humor has become one of the favorite
strategies used by creative people involved in the advertising
business. In fact among the various fields in which creative
language is used, advertising is probably the area of activity
in which creativity (and hence humor) is practiced with most
precise objectives.
From an applied AI point of view, we believe that an envi­
ronment for proposing solutions to advertising professionals
can be a realistic practical development of computational hu­
mor and one of the first attempts in dealing creative language.
Some examples of the huge array of opportunities that lan­
guage offers and that existent NLP techniques can cope with:
rhymes, wordplays, popular sayings or proverbs, quotations,
alliterations, triplets, chiasmus, neologism, non sequitur,
63
[Ephratt, 1990] M. Ephratt.
What's in a joke.
In
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Verlag, 1990.
[Fellbaum, 1998] C. Fellbaum. WordNet. An Electronic Lexical Database. The MIT Press, 1998.
[Giora and Fein, 1999] R. Giora and O. Fein. Irony: Context
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[Hofstadter et al., 1989] D.
Hofstadter,
L. Gabora,
V. Raskin, and S. Attardo. Synopsis of the work­
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[Hulstijn and Nijholt, 1996] Joris Hulstijn and Anton Ni­
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adaptation of existing expressions, adaptation of proverbs,
personification, synaesthesia (two or more senses combined).
An intelligent system devoted to advertisement, based on
an apparatus that extends the one we have described, can play
with these expressions, alter them, change the context, use
them as a platform for completely new ideas (e.g. create the
slogan: Thirst come, thirst served for a soft drink).
In the longer run the aim is to go for individual-oriented
advertisement. Let us leave aside issues of privacy for the
moment, though we know this is a critical theme. The availability of individual profiles, automatic inferencing of interest
and behavior models, information about the location of the
individual, combined with reasoning on the offer side (what
is advisable to promote in a given context of business) will
all provide the basic input for advertising things worth get­
ting the interest of a specific individual in a given context.
Commercial advertisement will be just one case along this
line - one can think of influencing the individual behaviour on
the basis of the availability of any possible resources such as,
for instance, cultural goods. A prediction is that when such
situation-oriented advertisements will become widespread,
the same features that prevail now with broadcasted material
will be sought after for the new case. So, forms of humorous
advertisement for the individual (even playing on personal as­
pects) will become a plus.
An important aspect to be taken into account is how humor
is appreciated by different individuals. Personality studies re­
garding this specific theme give important indications [Ruch,
20021. One option will also be to develop humor for conver­
sational systems, based on embodied agents. The work of Ni­
jholt [2002] Andre and Rist [2000] and Cassell [2001] could
provide the starting point for introducing dynamic humor.
For obtaining good results (there are not many things that
are worse than bad humor!) for these longer term objectives,
much work will be needed, especially further integration with
ontologies, common sense reasoning and pragmatics.
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ART AND CREATIVITY